Building a Vision Transformer-Based Damage Severity Classifier with Ground-Level Imagery of Homes Affected by California Wildfires

Fire Pub Date : 2024-04-11 DOI:10.3390/fire7040133
Kevin Luo, Ie-bin Lian
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Abstract

The increase in both the frequency and magnitude of natural disasters, coupled with recent advancements in artificial intelligence, has introduced prospects for investigating the potential of new technologies to facilitate disaster response processes. Preliminary Damage Assessment (PDA), a labor-intensive procedure necessitating manual examination of residential structures to ascertain post-disaster damage severity, stands to significantly benefit from the integration of computer vision-based classification algorithms, promising efficiency gains and heightened accuracy. Our paper proposes a Vision Transformer (ViT)-based model for classifying damage severity, achieving an accuracy rate of 95%. Notably, our model, trained on a repository of over 18,000 ground-level images of homes with damage severity annotated by damage assessment professionals during the 2020–2022 California wildfires, represents a novel application of ViT technology within this domain. Furthermore, we have open sourced this dataset—the first of its kind and scale—to be used by the research community. Additionally, we have developed a publicly accessible web application prototype built on this classification algorithm, which we have demonstrated to disaster management practitioners and received feedback on. Hence, our contribution to the literature encompasses the provision of a novel imagery dataset, an applied framework from field professionals, and a damage severity classification model with high accuracy.
利用受加州野火影响房屋的地面图像,构建基于视觉变压器的损坏严重程度分类器
自然灾害发生频率和规模的增加,再加上人工智能的最新进展,为研究新技术促进灾害响应过程的潜力带来了前景。初步损坏评估(PDA)是一项劳动密集型程序,需要人工检查住宅结构以确定灾后损坏的严重程度,而基于计算机视觉的分类算法的集成将大大提高效率和准确性。我们的论文提出了一种基于视觉转换器(ViT)的模型,用于对损坏严重程度进行分类,准确率达到 95%。值得注意的是,我们的模型是在一个由 2020-2022 年加州野火期间受损评估专业人员标注了受损严重程度的 18,000 多张房屋地面图像库中训练出来的,代表了 ViT 技术在这一领域的新应用。此外,我们还开放了该数据集的源代码,供研究界使用,这在同类数据集中尚属首次。此外,我们还在此分类算法的基础上开发了一个可公开访问的网络应用程序原型,并向灾害管理从业人员进行了演示和反馈。因此,我们对文献的贡献包括提供了一个新颖的图像数据集、一个来自现场专业人员的应用框架和一个高准确度的损害严重程度分类模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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